Adaptive noise model based iteratively reweighted penalized least squares for fluorescence background subtraction from Raman spectra

Abstract

The spectral analysis depends heavily on unwanted signals, such as the fluorescent background from the samples or other interfering components. A number of mathematical algorithms have been proposed to remove the background of Raman spectra. However, these methods require the selection of appropriate parameters to correct the of Raman spectra baseline. In this paper, we propose a method of adaptive noise model based on iteratively reweighted penalized least squares (ANM‐IRPLS) for Raman spectrum baseline correction. The algorithm was applied to various artificial spectra containing real forms of baselines and characteristic Raman peaks and then to the spectra of real drug samples with fluorescence obtained on a device equipped with a 532‐nm laser with a resolution of 15 cm−1. The modeling results showed that the proposed ANM‐IRPLS baseline correction method allows for better results in background removal than the airPLS. For real Raman spectra processed by the ANM‐IRPLS method, it is shown that the algorithm handles a complex background well, while maintaining the characteristic Raman signal features, such as a wide water peak for aqueous solutions.

Oxidative Dissolution Process of Sphalerite in Fe2(SO4)3-O3 System: Implications for Heavy Metals Removal and Recovery

“…Moreover, there is extra ozone depletion due to S 8 from the direct reaction of sphalerite and Fe 2 (SO 4 ) 3 . Finally, excessive Fe 2+ may consume hydroxyl radicals with high redox potential, as Zhang, Dong, and Yang [ 27 ] mentioned in Equation (4). Thus, 0.2 M Fe 2 (SO 4 ) 3 was chosen as one of the optimal conditions during sphalerite dissolution in the O 3 + Fe 2 (SO 4 ) 3 system.…”

“…The surface images of the residues were analyzed by scanning electron microscopy [ 27 ]. The S of different valence states on the leaching residue surface was detected by X-ray photoelectron spectroscopy (XPS) (ESCALAB 250Xi).…”

An adaptive extended Gaussian peak derivative reweighted penalised least squares method for baseline correction

“…The weight-update strategy of agdPLS added the derivative term of the tting differences, as shown in eqn (21).…”

“…11 In recent years, several research results in BC have been presented. [12][13][14] Common BC methods include polynomial tting, 15 segmented tting, 16 derivative spectroscopy, 17 wavelet transformation, 18 moving-window averaging, 19 robust baseline estimation, 20 penalised least squares (PLS), 21 morphological operators, 22 sparse representation, 23 and deep learning. 24 These methods have been widely used in pre-processing of spectral data and have effectively improved the accuracy of spectral analysis.…”